Regional Land Surface Conditions Developed Using the High‐Resolution Land Data Assimilation System: Challenges Over Complex Orography Himalayan Region
ABSTRACT The Uttarakhand state of India has been witnessing spatiotemporal variations in heavy rainfall, posing landslides, avalanches, and risks to livelihood and infrastructure. The complex terrain (ranging 250–~7500 m) and weather in this part of the Himalayan region pose difficulties in maintain...
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Wiley
2025-07-01
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| Series: | Meteorological Applications |
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| Online Access: | https://doi.org/10.1002/met.70072 |
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| author | Buri Vinodhkumar Krishna Kishore Osuri A. P. Dimri Sandipan Mukherjee Sami G. Al‐Ghamdi Dev Niyogi |
| author_facet | Buri Vinodhkumar Krishna Kishore Osuri A. P. Dimri Sandipan Mukherjee Sami G. Al‐Ghamdi Dev Niyogi |
| author_sort | Buri Vinodhkumar |
| collection | DOAJ |
| description | ABSTRACT The Uttarakhand state of India has been witnessing spatiotemporal variations in heavy rainfall, posing landslides, avalanches, and risks to livelihood and infrastructure. The complex terrain (ranging 250–~7500 m) and weather in this part of the Himalayan region pose difficulties in maintaining land surface observations, thus creating uncertainties in surface energy and hydrological processes. The present study demonstrates the value of the high‐resolution land data assimilation system (HRLDAS) integrated at 2 km grid spacing from 2011 to 2021 over Uttarakhand and validated against in situ, satellite, and reanalyzes products. Diurnal variation of sensible heat flux (SHF), and latent heat flux (LHF) are closer to the in situ observations (−35 to 64 Wm−2) than the global and regional analysis (−125 to 129 Wm−2 and −40 to 172 Wm−2) during monsoon season. The HRLDAS soil moisture (SM) is overestimated against in situ and exhibited less error against European Space Agency Climate Change Initiative (ESACCI) (0.02 m3 m−3 with 30%) and Cyclone Global Navigation Satellite System (CYGNSS) (−0.02 m3 m−3 error with 21%). The HRLDAS performs better for soil temperature (ST) with high correlation and less bias (0.94°C and −0.34°C) than the GLDAS (0.83°C and −0.61°C) and IMDAA (0.86°C and 2.2°C), when verified against in situ observations. The spatial distribution of HRLDAS shows maximum ST in the southern parts and minimum ST in the northern parts of the Uttarakhand region and is consistent with the GLDAS and IMDAA during monsoon. HRLDAS shows lesser biases in net radiation (12 Wm−2), SHF (−10 Wm−2), and LHF (9.7 Wm−2) compared to GLDAS (25, −17, 10.3 Wm−2), and IMDAA (38, −11, 16 Wm−2), respectively. Besides the performance, the HRLDAS products represent better spatial heterogeneity than the coarser global and regional analysis and are useful to initialize numerical models. |
| format | Article |
| id | doaj-art-cee56c6046ff4b4e8a58e5dcccaa4bbf |
| institution | Kabale University |
| issn | 1350-4827 1469-8080 |
| language | English |
| publishDate | 2025-07-01 |
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| series | Meteorological Applications |
| spelling | doaj-art-cee56c6046ff4b4e8a58e5dcccaa4bbf2025-08-26T11:54:48ZengWileyMeteorological Applications1350-48271469-80802025-07-01324n/an/a10.1002/met.70072Regional Land Surface Conditions Developed Using the High‐Resolution Land Data Assimilation System: Challenges Over Complex Orography Himalayan RegionBuri Vinodhkumar0Krishna Kishore Osuri1A. P. Dimri2Sandipan Mukherjee3Sami G. Al‐Ghamdi4Dev Niyogi5Department of Earth and Atmospheric Sciences National Institute of Technology Rourkela Rourkela Odisha IndiaDepartment of Earth and Atmospheric Sciences National Institute of Technology Rourkela Rourkela Odisha IndiaIndian Institute of Geomagnetism Mumbai Maharashtra IndiaGovind Ballabh Pant National Institute of Himalayan Environment Almora Uttarakhand IndiaBiological and Environmental Science and Engineering Division, Environmental Science and Engineering Program King Abdullah University of Science and Technology (KAUST) Thuwal Saudi ArabiaDepartment of Geological Sciences Jackson School of Geosciences, University of Texas at Austin Austin TX USAABSTRACT The Uttarakhand state of India has been witnessing spatiotemporal variations in heavy rainfall, posing landslides, avalanches, and risks to livelihood and infrastructure. The complex terrain (ranging 250–~7500 m) and weather in this part of the Himalayan region pose difficulties in maintaining land surface observations, thus creating uncertainties in surface energy and hydrological processes. The present study demonstrates the value of the high‐resolution land data assimilation system (HRLDAS) integrated at 2 km grid spacing from 2011 to 2021 over Uttarakhand and validated against in situ, satellite, and reanalyzes products. Diurnal variation of sensible heat flux (SHF), and latent heat flux (LHF) are closer to the in situ observations (−35 to 64 Wm−2) than the global and regional analysis (−125 to 129 Wm−2 and −40 to 172 Wm−2) during monsoon season. The HRLDAS soil moisture (SM) is overestimated against in situ and exhibited less error against European Space Agency Climate Change Initiative (ESACCI) (0.02 m3 m−3 with 30%) and Cyclone Global Navigation Satellite System (CYGNSS) (−0.02 m3 m−3 error with 21%). The HRLDAS performs better for soil temperature (ST) with high correlation and less bias (0.94°C and −0.34°C) than the GLDAS (0.83°C and −0.61°C) and IMDAA (0.86°C and 2.2°C), when verified against in situ observations. The spatial distribution of HRLDAS shows maximum ST in the southern parts and minimum ST in the northern parts of the Uttarakhand region and is consistent with the GLDAS and IMDAA during monsoon. HRLDAS shows lesser biases in net radiation (12 Wm−2), SHF (−10 Wm−2), and LHF (9.7 Wm−2) compared to GLDAS (25, −17, 10.3 Wm−2), and IMDAA (38, −11, 16 Wm−2), respectively. Besides the performance, the HRLDAS products represent better spatial heterogeneity than the coarser global and regional analysis and are useful to initialize numerical models.https://doi.org/10.1002/met.70072CYGNSSESACCIfoothills of HimalayasHRLDASsoil moisture and soil temperatureUttarakhand |
| spellingShingle | Buri Vinodhkumar Krishna Kishore Osuri A. P. Dimri Sandipan Mukherjee Sami G. Al‐Ghamdi Dev Niyogi Regional Land Surface Conditions Developed Using the High‐Resolution Land Data Assimilation System: Challenges Over Complex Orography Himalayan Region Meteorological Applications CYGNSS ESACCI foothills of Himalayas HRLDAS soil moisture and soil temperature Uttarakhand |
| title | Regional Land Surface Conditions Developed Using the High‐Resolution Land Data Assimilation System: Challenges Over Complex Orography Himalayan Region |
| title_full | Regional Land Surface Conditions Developed Using the High‐Resolution Land Data Assimilation System: Challenges Over Complex Orography Himalayan Region |
| title_fullStr | Regional Land Surface Conditions Developed Using the High‐Resolution Land Data Assimilation System: Challenges Over Complex Orography Himalayan Region |
| title_full_unstemmed | Regional Land Surface Conditions Developed Using the High‐Resolution Land Data Assimilation System: Challenges Over Complex Orography Himalayan Region |
| title_short | Regional Land Surface Conditions Developed Using the High‐Resolution Land Data Assimilation System: Challenges Over Complex Orography Himalayan Region |
| title_sort | regional land surface conditions developed using the high resolution land data assimilation system challenges over complex orography himalayan region |
| topic | CYGNSS ESACCI foothills of Himalayas HRLDAS soil moisture and soil temperature Uttarakhand |
| url | https://doi.org/10.1002/met.70072 |
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